My question is more about experimental design rather than program usage

Has anybody explored the effect of sampling bias on mcrca estimation?

I have seen published statements that the data set was pruned to avoid
unequal taxonomic repetition, but no reference was given. Intuitively this
seem sensible but is there evidence?

In my case this is relevant for two reasons

1) I'm planning to use calibration points from a sister group (also
estimated using BEAST).

The question is now whether I should add the entire data of this sister
group to my data or whether it is sufficient to include the minimum number
of taxa which represent the nodes for which mcrca estimates exist.

2) In my own data set a number of species are represented with multiple
haplotypes extracted from large phylogeographic data sets whereas other
species are only represented by a single individual. Should I prune the
data set so that each species and clade is equally represented?

Thanks
Bernd

Hi Bernd,

I'm also concerned with sampling bias and I think that your question has no
answer yet, at least there's no empirical evidence[to my knowledge] . I'm
planning on doing some research about the effects of sampling bias on the
estimation of phylogeographic parameters in BEAST and ways to cope with it.
So far I contacted Nuno Faria (PhD candidate, KU Leuven University,
Belgium) and Joe Parker (Postdoc, University of London), trying to set up a
working group on the issue. Additionally I'm conducting some literature
search to see what people actually do about it. As you said, there's random
stratified sampling [ which, in my opinion, would be best under
an homogeneity assumption] and there's prior modification to incorporate
the effects of using a biased sample (see Faria et, 2012, Journal of
General Virology).
In this way, I think that (i) there's no solid answer to your doubts and
(ii) if you're interested, we could investigate it together.

Best,

Luiz, PAHO/WHO Reference Lab on Foot-and-Mouth Disease, Brazil.